{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0aca99c8",
   "metadata": {},
   "source": [
    "# Baseline analysis\n",
    "\n",
    "New simple baselines:\n",
    "\n",
    "- DeviatingFromMean: Takes the mean of the entire TS and computes the deviation to it for each individual point as anomaly score\n",
    "- DeviatingFromMedian: Takes the median of the entire TS and computes the deviation to it for each individual point as anomaly score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0495c0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import re\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "from IPython.display import display, Markdown, Latex\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 8)\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import MultiDatasetManager"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984b06e8",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1176a021",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5aaf61af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Benchmark data path: /home/sebastian/Documents/projects/akita/data/benchmark-data/data-processed\n",
      "GutenTAG data path: /home/sebastian/Documents/projects/akita/data/test-cases\n",
      "Correlation data path: /home/sebastian/Documents/projects/akita/data/correlation-anomalies\n",
      "Result path: /home/sebastian/Documents/projects/akita/results/2022-01-18_deviating-baselines\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "root_path = Path(\"../..\").resolve()\n",
    "benchmark_data_path = root_path / \"data\" / \"benchmark-data\" / \"data-processed\"\n",
    "gutentag_data_path = root_path / \"data\" / \"test-cases\"\n",
    "correlation_data_path = root_path / \"data\" / \"correlation-anomalies\"\n",
    "result_root_path = root_path / \"results\"\n",
    "experiment_result_folder = \"2022-01-18_deviating-baselines\"\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Benchmark data path: {benchmark_data_path.resolve()}\")\n",
    "print(f\"GutenTAG data path: {gutentag_data_path.resolve()}\")\n",
    "print(f\"Correlation data path: {correlation_data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a910fce",
   "metadata": {},
   "source": [
    "Load results and dataset metadata and define utility functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3666f8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/sebastian/Documents/projects/akita/results/2022-01-18_deviating-baselines\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# aggregate runtime\n",
    "df[\"overall_time\"] = df[\"execute_main_time\"].fillna(0) + df[\"train_main_time\"].fillna(0)\n",
    "\n",
    "# add RANGE_PR_AUC if it is not part of the results\n",
    "if \"RANGE_PR_AUC\" not in df.columns:\n",
    "    df[\"RANGE_PR_AUC\"] = np.nan\n",
    "\n",
    "# remove all duplicates (not necessary, but sometimes, we have some)\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "# load dataset details\n",
    "dmgr = MultiDatasetManager([\n",
    "    benchmark_data_path,\n",
    "    gutentag_data_path,\n",
    "    correlation_data_path\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e91f1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "41f73bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = False\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_id, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if not isinstance(algorithm_name, list):\n",
    "        algorithms = [algorithm_name]\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # deconstruct dataset ID\n",
    "    collection_name, dataset_name = dataset_id\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[(df[\"collection\"] == collection_name) & (df[\"dataset\"] == dataset_name), \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo in algorithms:\n",
    "        algos.append(algo)\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[algo] = df.loc[(df[\"algorithm\"] == algo) & (df[\"collection\"] == collection_name) & (df[\"dataset\"] == dataset_name), \"ROC_AUC\"].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} was not executed on {dataset_id}.\")\n",
    "            auroc[algo] = -1\n",
    "            skip_algos.append(algo)\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[algo] = load_scores_df(algo, dataset_id).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_id}.\")\n",
    "            df_scores[algo] = np.nan\n",
    "            skip_algos.append(algo)\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=df_dataset.columns[i][:30]), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for algo in algorithms:\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[algo], name=f\"{algo}={auroc[algo]:.4f}\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique(algorithms))} on {dataset_id}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=df_dataset.columns[i][:30])\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for algo in algorithms:\n",
    "        axs[1].plot(df_scores.index, df_scores[algo], label=f\"{algo}={auroc[algo]:.4f}\")\n",
    "    if len(df_dataset.columns) < 15:\n",
    "        axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique(algorithms))} on {dataset_id}\")\n",
    "    fig.tight_layout()\n",
    "    plt.close()\n",
    "    return fig\n",
    "\n",
    "def plot_boxplot(df, n_show = 20, title=\"Box plots\", ax_label=\"values\", legend_title=\"Algorithms\", fmt_label=lambda x: x, fliers=False, use_plotly=default_use_plotly):\n",
    "    n_show = n_show // 2\n",
    "    title = title + f\" (worst {n_show} and best {n_show} {legend_title.lower()})\"\n",
    "    \n",
    "    if use_plotly:\n",
    "        import plotly.offline as py\n",
    "        import plotly.graph_objects as go\n",
    "        import plotly.figure_factory as ff\n",
    "        import plotly.express as px\n",
    "        from plotly.subplots import make_subplots\n",
    "        \n",
    "        fig = go.Figure()\n",
    "        for i, c in enumerate(df.columns):\n",
    "            fig.add_trace(go.Box(\n",
    "                x=df[c],\n",
    "                name=fmt_label(c),\n",
    "                boxpoints=False,\n",
    "                visible=None if i < n_show or i > len(df.columns)-n_show-1 else \"legendonly\"\n",
    "            ))\n",
    "        fig.update_layout(\n",
    "            title={\"text\": title, \"xanchor\": \"center\", \"x\": 0.5},\n",
    "            xaxis_title=ax_label,\n",
    "            legend_title=legend_title\n",
    "        )\n",
    "        return py.iplot(fig)\n",
    "    else:\n",
    "        df_boxplot = pd.concat([df.iloc[:, :n_show], df.iloc[:, -n_show:]])\n",
    "        labels = df_boxplot.columns\n",
    "        labels = [fmt_label(c) for c in labels]\n",
    "        values = [df_boxplot[c].dropna().values for c in df_boxplot.columns]\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.boxplot(values, sym=None if fliers else \"\", vert=True, meanline=True, showmeans=True, showfliers=fliers, manage_ticks=True)\n",
    "        ax.set_ylabel(ax_label)\n",
    "        ax.set_title(title)\n",
    "        ax.set_xticklabels(labels, rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "        # add vline to separate bad and good algos\n",
    "        ymin, ymax = ax.get_ylim()\n",
    "        ax.vlines([n_show + 0.5], ymin, ymax, colors=\"black\", linestyles=\"dashed\")\n",
    "        fig.tight_layout()\n",
    "        plt.close()\n",
    "        return fig\n",
    "\n",
    "def plot_algorithm_bars(df, y_name=\"ROC_AUC\", title=\"Bar chart for algorithms\", use_plotly=default_use_plotly):\n",
    "    if use_plotly:\n",
    "        fig = px.bar(df, x=\"algorithm\", y=y_name)\n",
    "        return py.iplot(fig)\n",
    "    else:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.bar(df[\"algorithm\"], df[y_name], label=y_name)\n",
    "        ax.set_ylabel(y_name)\n",
    "        ax.set_title(title)\n",
    "        ax.set_xticklabels(df[\"algorithm\"], rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "        ax.legend()\n",
    "        plt.close()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bca4a3b",
   "metadata": {},
   "source": [
    "## Execution Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e59f41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>collection</th>\n",
       "      <th>dataset</th>\n",
       "      <th>status</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>AVERAGE_PRECISION</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>RANGE_PR_AUC</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>hyper_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeviatingFromMean</td>\n",
       "      <td>SWaT</td>\n",
       "      <td>SWaT-A1&amp;A2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.121320</td>\n",
       "      <td>0.560660</td>\n",
       "      <td>0.560660</td>\n",
       "      <td>1.013289</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DeviatingFromMean</td>\n",
       "      <td>SWaT</td>\n",
       "      <td>SWaT-A5</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.139304</td>\n",
       "      <td>0.569652</td>\n",
       "      <td>0.569652</td>\n",
       "      <td>0.055516</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DeviatingFromMean</td>\n",
       "      <td>SWaT</td>\n",
       "      <td>SWaT-A6</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.068555</td>\n",
       "      <td>0.534278</td>\n",
       "      <td>0.534278</td>\n",
       "      <td>0.048667</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DeviatingFromMean</td>\n",
       "      <td>WADI</td>\n",
       "      <td>WADI.A2-1</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.057737</td>\n",
       "      <td>0.528868</td>\n",
       "      <td>0.528868</td>\n",
       "      <td>1.145024</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DeviatingFromMean</td>\n",
       "      <td>WADI</td>\n",
       "      <td>WADI.A2-2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.057737</td>\n",
       "      <td>0.528868</td>\n",
       "      <td>0.528868</td>\n",
       "      <td>1.106095</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>DeviatingFromMedian</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-trend.supervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.479022</td>\n",
       "      <td>0.009088</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.009842</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>DeviatingFromMedian</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-trend.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.479022</td>\n",
       "      <td>0.009088</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.009842</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3099</th>\n",
       "      <td>DeviatingFromMedian</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-variance.semi-supervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.508442</td>\n",
       "      <td>0.143463</td>\n",
       "      <td>0.143096</td>\n",
       "      <td>0.116722</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>DeviatingFromMedian</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-variance.supervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.508442</td>\n",
       "      <td>0.143463</td>\n",
       "      <td>0.143096</td>\n",
       "      <td>0.116722</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101</th>\n",
       "      <td>DeviatingFromMedian</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-variance.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.508442</td>\n",
       "      <td>0.143463</td>\n",
       "      <td>0.143096</td>\n",
       "      <td>0.116722</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3092 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                algorithm collection                              dataset  \\\n",
       "0       DeviatingFromMean       SWaT                           SWaT-A1&A2   \n",
       "1       DeviatingFromMean       SWaT                              SWaT-A5   \n",
       "2       DeviatingFromMean       SWaT                              SWaT-A6   \n",
       "3       DeviatingFromMean       WADI                            WADI.A2-1   \n",
       "4       DeviatingFromMean       WADI                            WADI.A2-2   \n",
       "...                   ...        ...                                  ...   \n",
       "3097  DeviatingFromMedian   GutenTAG          sinus-type-trend.supervised   \n",
       "3098  DeviatingFromMedian   GutenTAG        sinus-type-trend.unsupervised   \n",
       "3099  DeviatingFromMedian   GutenTAG  sinus-type-variance.semi-supervised   \n",
       "3100  DeviatingFromMedian   GutenTAG       sinus-type-variance.supervised   \n",
       "3101  DeviatingFromMedian   GutenTAG     sinus-type-variance.unsupervised   \n",
       "\n",
       "         status   ROC_AUC  AVERAGE_PRECISION    PR_AUC  RANGE_PR_AUC  \\\n",
       "0     Status.OK  0.500000           0.121320  0.560660      0.560660   \n",
       "1     Status.OK  0.500000           0.139304  0.569652      0.569652   \n",
       "2     Status.OK  0.500000           0.068555  0.534278      0.534278   \n",
       "3     Status.OK  0.500000           0.057737  0.528868      0.528868   \n",
       "4     Status.OK  0.500000           0.057737  0.528868      0.528868   \n",
       "...         ...       ...                ...       ...           ...   \n",
       "3097  Status.OK  0.479022           0.009088  0.008942      0.009842   \n",
       "3098  Status.OK  0.479022           0.009088  0.008942      0.009842   \n",
       "3099  Status.OK  0.508442           0.143463  0.143096      0.116722   \n",
       "3100  Status.OK  0.508442           0.143463  0.143096      0.116722   \n",
       "3101  Status.OK  0.508442           0.143463  0.143096      0.116722   \n",
       "\n",
       "      execute_main_time hyper_params  \n",
       "0              1.013289           {}  \n",
       "1              0.055516           {}  \n",
       "2              0.048667           {}  \n",
       "3              1.145024           {}  \n",
       "4              1.106095           {}  \n",
       "...                 ...          ...  \n",
       "3097           0.000648           {}  \n",
       "3098           0.001302           {}  \n",
       "3099           0.000695           {}  \n",
       "3100           0.000803           {}  \n",
       "3101           0.001012           {}  \n",
       "\n",
       "[3092 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"algorithm\", \"collection\", \"dataset\", \"status\", \"ROC_AUC\", \"AVERAGE_PRECISION\", \"PR_AUC\", \"RANGE_PR_AUC\", \"execute_main_time\", \"hyper_params\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9ad859e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>status</th>\n",
       "      <th>Status.OK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMean</th>\n",
       "      <td>1546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "      <td>1546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status               Status.OK\n",
       "algorithm                     \n",
       "DeviatingFromMean         1546\n",
       "DeviatingFromMedian       1546"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error_counts = df.pivot_table(index=\"algorithm\", columns=[\"status\"], values=\"repetition\", aggfunc=\"count\")\n",
    "df_error_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f7892e",
   "metadata": {},
   "source": [
    "## ROC_AUC scores\n",
    "\n",
    "### Per algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9d8b05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian/.conda/envs/timeeval/lib/python3.8/site-packages/pandas/core/generic.py:4150: PerformanceWarning:\n",
      "\n",
      "dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>algorithm</th>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "      <th>DeviatingFromMean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.605568</td>\n",
       "      <td>0.601494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.561186</td>\n",
       "      <td>0.555375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "algorithm  DeviatingFromMedian  DeviatingFromMean\n",
       "min                   0.000051           0.000051\n",
       "mean                  0.605568           0.601494\n",
       "median                0.561186           0.555375\n",
       "max                   1.000000           1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggregations = [\"min\", \"mean\", \"median\", \"max\"]\n",
    "dominant_aggregation = \"mean\"\n",
    "\n",
    "df_overall_scores = df.pivot_table(index=\"algorithm\", values=\"ROC_AUC\", aggfunc=aggregations)\n",
    "df_overall_scores.columns = aggregations\n",
    "df_overall_scores = df_overall_scores.sort_values(by=dominant_aggregation, ascending=False)\n",
    "\n",
    "\n",
    "df_asl = df.pivot(index=\"algorithm\", columns=[\"collection\", \"dataset\"], values=\"ROC_AUC\")\n",
    "df_asl = df_asl.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl[dominant_aggregation] = df_asl.agg(dominant_aggregation, axis=1)\n",
    "df_asl = df_asl.sort_values(by=dominant_aggregation, ascending=True)\n",
    "df_asl = df_asl.drop(columns=dominant_aggregation).T\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_overall_scores.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b9a201e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_boxplot(df_asl, title=\"AUC_ROC box plots\", ax_label=\"AUC_ROC score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e181bb07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_name = (\"GutenTAG\", \"rw-diff-count-5.unsupervised\")\n",
    "algorithm_name = None\n",
    "n_algos = 4\n",
    "\n",
    "plot_scores(algorithm_name if algorithm_name else df_asl.columns[-n_algos:].to_list(), dataset_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c57afe",
   "metadata": {},
   "source": [
    "### Per dataset collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "810842d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>collection</th>\n",
       "      <th>Exathlon</th>\n",
       "      <th>CalIt2</th>\n",
       "      <th>IOPS</th>\n",
       "      <th>WebscopeS5</th>\n",
       "      <th>Genesis</th>\n",
       "      <th>NASA-MSL</th>\n",
       "      <th>Metro</th>\n",
       "      <th>GHL</th>\n",
       "      <th>MITDB</th>\n",
       "      <th>NASA-SMAP</th>\n",
       "      <th>SVDB</th>\n",
       "      <th>MGAB</th>\n",
       "      <th>KDD-TSAD</th>\n",
       "      <th>NAB</th>\n",
       "      <th>GutenTAG</th>\n",
       "      <th>Daphnet</th>\n",
       "      <th>Keogh</th>\n",
       "      <th>OPPORTUNITY</th>\n",
       "      <th>SMD</th>\n",
       "      <th>SWaT</th>\n",
       "      <th>WADI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.998207</td>\n",
       "      <td>0.786603</td>\n",
       "      <td>0.621566</td>\n",
       "      <td>0.003521</td>\n",
       "      <td>0.691755</td>\n",
       "      <td>0.434652</td>\n",
       "      <td>0.665365</td>\n",
       "      <td>0.053317</td>\n",
       "      <td>0.381707</td>\n",
       "      <td>0.138749</td>\n",
       "      <td>0.458393</td>\n",
       "      <td>0.498978</td>\n",
       "      <td>0.024023</td>\n",
       "      <td>0.252395</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.397008</td>\n",
       "      <td>0.293411</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.998962</td>\n",
       "      <td>0.836106</td>\n",
       "      <td>0.796602</td>\n",
       "      <td>0.782318</td>\n",
       "      <td>0.725302</td>\n",
       "      <td>0.689170</td>\n",
       "      <td>0.667648</td>\n",
       "      <td>0.652068</td>\n",
       "      <td>0.623204</td>\n",
       "      <td>0.594726</td>\n",
       "      <td>0.581375</td>\n",
       "      <td>0.558623</td>\n",
       "      <td>0.547376</td>\n",
       "      <td>0.546952</td>\n",
       "      <td>0.527715</td>\n",
       "      <td>0.516615</td>\n",
       "      <td>0.511727</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.999029</td>\n",
       "      <td>0.836106</td>\n",
       "      <td>0.844732</td>\n",
       "      <td>0.847786</td>\n",
       "      <td>0.725302</td>\n",
       "      <td>0.671596</td>\n",
       "      <td>0.667648</td>\n",
       "      <td>0.787388</td>\n",
       "      <td>0.610672</td>\n",
       "      <td>0.580288</td>\n",
       "      <td>0.572922</td>\n",
       "      <td>0.557900</td>\n",
       "      <td>0.533789</td>\n",
       "      <td>0.531292</td>\n",
       "      <td>0.513870</td>\n",
       "      <td>0.507287</td>\n",
       "      <td>0.572321</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.999731</td>\n",
       "      <td>0.885610</td>\n",
       "      <td>0.871582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758848</td>\n",
       "      <td>0.962332</td>\n",
       "      <td>0.669931</td>\n",
       "      <td>0.995350</td>\n",
       "      <td>0.974781</td>\n",
       "      <td>0.929298</td>\n",
       "      <td>0.726992</td>\n",
       "      <td>0.642839</td>\n",
       "      <td>0.997891</td>\n",
       "      <td>0.832167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.633607</td>\n",
       "      <td>0.672971</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "collection  Exathlon    CalIt2      IOPS  WebscopeS5   Genesis  NASA-MSL  \\\n",
       "min         0.998207  0.786603  0.621566    0.003521  0.691755  0.434652   \n",
       "mean        0.998962  0.836106  0.796602    0.782318  0.725302  0.689170   \n",
       "median      0.999029  0.836106  0.844732    0.847786  0.725302  0.671596   \n",
       "max         0.999731  0.885610  0.871582    1.000000  0.758848  0.962332   \n",
       "\n",
       "collection     Metro       GHL     MITDB  NASA-SMAP      SVDB      MGAB  \\\n",
       "min         0.665365  0.053317  0.381707   0.138749  0.458393  0.498978   \n",
       "mean        0.667648  0.652068  0.623204   0.594726  0.581375  0.558623   \n",
       "median      0.667648  0.787388  0.610672   0.580288  0.572922  0.557900   \n",
       "max         0.669931  0.995350  0.974781   0.929298  0.726992  0.642839   \n",
       "\n",
       "collection  KDD-TSAD       NAB  GutenTAG   Daphnet     Keogh  OPPORTUNITY  \\\n",
       "min         0.024023  0.252395  0.000051  0.397008  0.293411          0.5   \n",
       "mean        0.547376  0.546952  0.527715  0.516615  0.511727          0.5   \n",
       "median      0.533789  0.531292  0.513870  0.507287  0.572321          0.5   \n",
       "max         0.997891  0.832167  1.000000  0.633607  0.672971          0.5   \n",
       "\n",
       "collection  SMD  SWaT  WADI  \n",
       "min         0.5   0.5   0.5  \n",
       "mean        0.5   0.5   0.5  \n",
       "median      0.5   0.5   0.5  \n",
       "max         0.5   0.5   0.5  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggregations = [\"min\", \"mean\", \"median\", \"max\"]\n",
    "dominant_aggregation = \"mean\"\n",
    "\n",
    "df_collection_scores = df.pivot_table(index=\"collection\", values=\"ROC_AUC\", aggfunc=aggregations)\n",
    "df_collection_scores.columns = aggregations\n",
    "df_collection_scores = df_collection_scores.sort_values(by=dominant_aggregation, ascending=False)\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_collection_scores.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ece1309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp = df.pivot(index=\"collection\", columns=[\"algorithm\", \"dataset\"], values=\"ROC_AUC\")\n",
    "df_tmp = df_tmp.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_tmp[dominant_aggregation] = df_tmp.agg(dominant_aggregation, axis=1)\n",
    "df_tmp = df_tmp.sort_values(by=dominant_aggregation, ascending=True)\n",
    "df_tmp = df_tmp.drop(columns=dominant_aggregation).T\n",
    "\n",
    "plot_boxplot(df_tmp, title=\"AUC_ROC box plots\", ax_label=\"AUC_ROC score\", legend_title=\"Collections\", n_show=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21735cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_scores([\"DeviatingFromMean\", \"DeviatingFromMedian\"], (\"GutenTAG\", \"poly-channels-single-of-5.unsupervised\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3138988e",
   "metadata": {},
   "source": [
    "## Dataset assessment\n",
    "\n",
    "Very easy datasets, because they can be solved by the baselines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27f9ecab",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ROC_AUC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>DeviatingFromMean</th>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "      <th>DeviatingFromMean</th>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection</th>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Exathlon</th>\n",
       "      <th>4_1_100000_61-29</th>\n",
       "      <td>0.994422</td>\n",
       "      <td>0.995992</td>\n",
       "      <td>0.998207</td>\n",
       "      <td>0.999029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_1_100000_61-30</th>\n",
       "      <td>0.994422</td>\n",
       "      <td>0.995992</td>\n",
       "      <td>0.998207</td>\n",
       "      <td>0.999029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_1_100000_63-64</th>\n",
       "      <td>0.994897</td>\n",
       "      <td>0.996219</td>\n",
       "      <td>0.999391</td>\n",
       "      <td>0.999731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_1_100000_64-63</th>\n",
       "      <td>0.984559</td>\n",
       "      <td>0.987123</td>\n",
       "      <td>0.998941</td>\n",
       "      <td>0.999165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"36\" valign=\"top\">GutenTAG</th>\n",
       "      <th>cbf-length-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbf-length-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbf-length-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbf-type-extremum.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbf-type-extremum.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cbf-type-extremum.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-length-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-length-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-length-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-type-extremum.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-type-extremum.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecg-type-extremum.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-diff-count-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-diff-count-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-diff-count-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-length-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-length-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-length-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-type-platform.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-type-platform.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly-type-platform.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-diff-count-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-diff-count-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-diff-count-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-length-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-length-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-length-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-type-extremum.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-type-extremum.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rw-type-extremum.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-length-1.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-length-1.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-length-1.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-type-extremum.semi-supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-type-extremum.supervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinus-type-extremum.unsupervised</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NASA-MSL</th>\n",
       "      <th>D-14</th>\n",
       "      <td>0.925776</td>\n",
       "      <td>0.925776</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.918919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M-6</th>\n",
       "      <td>0.889378</td>\n",
       "      <td>0.905552</td>\n",
       "      <td>0.926337</td>\n",
       "      <td>0.962332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASA-SMAP</th>\n",
       "      <th>A-6</th>\n",
       "      <td>0.787956</td>\n",
       "      <td>0.787956</td>\n",
       "      <td>0.929298</td>\n",
       "      <td>0.929298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"121\" valign=\"top\">WebscopeS5</th>\n",
       "      <th>A1Benchmark-1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-11</th>\n",
       "      <td>0.936218</td>\n",
       "      <td>0.938956</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>0.993291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-12</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-13</th>\n",
       "      <td>0.801014</td>\n",
       "      <td>0.792966</td>\n",
       "      <td>0.982831</td>\n",
       "      <td>0.979999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-15</th>\n",
       "      <td>0.940399</td>\n",
       "      <td>0.934524</td>\n",
       "      <td>0.999020</td>\n",
       "      <td>0.998740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-16</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-18</th>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.293622</td>\n",
       "      <td>0.998628</td>\n",
       "      <td>0.988112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-2</th>\n",
       "      <td>0.835022</td>\n",
       "      <td>0.835458</td>\n",
       "      <td>0.976458</td>\n",
       "      <td>0.977117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-21</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-22</th>\n",
       "      <td>0.957058</td>\n",
       "      <td>0.974266</td>\n",
       "      <td>0.981940</td>\n",
       "      <td>0.998468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-23</th>\n",
       "      <td>0.838277</td>\n",
       "      <td>0.838277</td>\n",
       "      <td>0.999286</td>\n",
       "      <td>0.999286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-24</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-25</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-30</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-33</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-34</th>\n",
       "      <td>0.906463</td>\n",
       "      <td>0.869671</td>\n",
       "      <td>0.999497</td>\n",
       "      <td>0.999396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-36</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-39</th>\n",
       "      <td>0.775553</td>\n",
       "      <td>0.776393</td>\n",
       "      <td>0.990967</td>\n",
       "      <td>0.991602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-41</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-42</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-44</th>\n",
       "      <td>0.789563</td>\n",
       "      <td>0.789563</td>\n",
       "      <td>0.952097</td>\n",
       "      <td>0.952097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-45</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-49</th>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.293622</td>\n",
       "      <td>0.998628</td>\n",
       "      <td>0.988112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-50</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-53</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-54</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-58</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-6</th>\n",
       "      <td>0.880659</td>\n",
       "      <td>0.880022</td>\n",
       "      <td>0.986198</td>\n",
       "      <td>0.984364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-60</th>\n",
       "      <td>0.972133</td>\n",
       "      <td>0.972133</td>\n",
       "      <td>0.999654</td>\n",
       "      <td>0.999654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-62</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-63</th>\n",
       "      <td>0.886222</td>\n",
       "      <td>0.887082</td>\n",
       "      <td>0.997642</td>\n",
       "      <td>0.997729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-66</th>\n",
       "      <td>0.907345</td>\n",
       "      <td>0.907166</td>\n",
       "      <td>0.947510</td>\n",
       "      <td>0.941265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-67</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Benchmark-9</th>\n",
       "      <td>0.898115</td>\n",
       "      <td>0.898115</td>\n",
       "      <td>0.999570</td>\n",
       "      <td>0.999570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-100</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-14</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-16</th>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.999294</td>\n",
       "      <td>0.999294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-19</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-21</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-23</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-25</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-27</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-28</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-29</th>\n",
       "      <td>0.751190</td>\n",
       "      <td>0.771864</td>\n",
       "      <td>0.997639</td>\n",
       "      <td>0.998111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-30</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-31</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-33</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-34</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-35</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-37</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-39</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-41</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-42</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-43</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-44</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-46</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-47</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-48</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-49</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-51</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-53</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-55</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-56</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-57</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-58</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-59</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-60</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-61</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-62</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-65</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-67</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-69</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-70</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-71</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-72</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-73</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-74</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-75</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-76</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-77</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-79</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-81</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-83</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-84</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-85</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-86</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-87</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-88</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-89</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-90</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-91</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-93</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-94</th>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.998941</td>\n",
       "      <td>0.998941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-95</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-97</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-98</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2Benchmark-99</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-12</th>\n",
       "      <td>0.715359</td>\n",
       "      <td>0.715763</td>\n",
       "      <td>0.967883</td>\n",
       "      <td>0.968529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-23</th>\n",
       "      <td>0.760246</td>\n",
       "      <td>0.764227</td>\n",
       "      <td>0.970330</td>\n",
       "      <td>0.969489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-33</th>\n",
       "      <td>0.734135</td>\n",
       "      <td>0.718026</td>\n",
       "      <td>0.989533</td>\n",
       "      <td>0.989010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-6</th>\n",
       "      <td>0.846515</td>\n",
       "      <td>0.846515</td>\n",
       "      <td>0.993628</td>\n",
       "      <td>0.993628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-66</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-69</th>\n",
       "      <td>0.780244</td>\n",
       "      <td>0.780244</td>\n",
       "      <td>0.996414</td>\n",
       "      <td>0.996414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-7</th>\n",
       "      <td>0.822195</td>\n",
       "      <td>0.824625</td>\n",
       "      <td>0.995701</td>\n",
       "      <td>0.996179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-76</th>\n",
       "      <td>0.735988</td>\n",
       "      <td>0.764226</td>\n",
       "      <td>0.988776</td>\n",
       "      <td>0.988776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-77</th>\n",
       "      <td>0.828528</td>\n",
       "      <td>0.828434</td>\n",
       "      <td>0.974647</td>\n",
       "      <td>0.974647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-86</th>\n",
       "      <td>0.721699</td>\n",
       "      <td>0.724232</td>\n",
       "      <td>0.966545</td>\n",
       "      <td>0.966314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-91</th>\n",
       "      <td>0.804626</td>\n",
       "      <td>0.804626</td>\n",
       "      <td>0.977313</td>\n",
       "      <td>0.977313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3Benchmark-92</th>\n",
       "      <td>0.754326</td>\n",
       "      <td>0.751606</td>\n",
       "      <td>0.993496</td>\n",
       "      <td>0.993645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          PR_AUC  \\\n",
       "algorithm                                      DeviatingFromMean   \n",
       "collection dataset                                                 \n",
       "Exathlon   4_1_100000_61-29                             0.994422   \n",
       "           4_1_100000_61-30                             0.994422   \n",
       "           5_1_100000_63-64                             0.994897   \n",
       "           5_1_100000_64-63                             0.984559   \n",
       "GutenTAG   cbf-length-1.semi-supervised                 1.000000   \n",
       "           cbf-length-1.supervised                      1.000000   \n",
       "           cbf-length-1.unsupervised                    1.000000   \n",
       "           cbf-type-extremum.semi-supervised            1.000000   \n",
       "           cbf-type-extremum.supervised                 1.000000   \n",
       "           cbf-type-extremum.unsupervised               1.000000   \n",
       "           ecg-length-1.semi-supervised                 1.000000   \n",
       "           ecg-length-1.supervised                      1.000000   \n",
       "           ecg-length-1.unsupervised                    1.000000   \n",
       "           ecg-type-extremum.semi-supervised            1.000000   \n",
       "           ecg-type-extremum.supervised                 1.000000   \n",
       "           ecg-type-extremum.unsupervised               1.000000   \n",
       "           poly-diff-count-1.semi-supervised            1.000000   \n",
       "           poly-diff-count-1.supervised                 1.000000   \n",
       "           poly-diff-count-1.unsupervised               1.000000   \n",
       "           poly-length-1.semi-supervised                1.000000   \n",
       "           poly-length-1.supervised                     1.000000   \n",
       "           poly-length-1.unsupervised                   1.000000   \n",
       "           poly-type-platform.semi-supervised           1.000000   \n",
       "           poly-type-platform.supervised                1.000000   \n",
       "           poly-type-platform.unsupervised              1.000000   \n",
       "           rw-diff-count-1.semi-supervised              1.000000   \n",
       "           rw-diff-count-1.supervised                   1.000000   \n",
       "           rw-diff-count-1.unsupervised                 1.000000   \n",
       "           rw-length-1.semi-supervised                  1.000000   \n",
       "           rw-length-1.supervised                       1.000000   \n",
       "           rw-length-1.unsupervised                     1.000000   \n",
       "           rw-type-extremum.semi-supervised             1.000000   \n",
       "           rw-type-extremum.supervised                  1.000000   \n",
       "           rw-type-extremum.unsupervised                1.000000   \n",
       "           sinus-length-1.semi-supervised               1.000000   \n",
       "           sinus-length-1.supervised                    1.000000   \n",
       "           sinus-length-1.unsupervised                  1.000000   \n",
       "           sinus-type-extremum.semi-supervised          1.000000   \n",
       "           sinus-type-extremum.supervised               1.000000   \n",
       "           sinus-type-extremum.unsupervised             1.000000   \n",
       "NASA-MSL   D-14                                         0.925776   \n",
       "           M-6                                          0.889378   \n",
       "NASA-SMAP  A-6                                          0.787956   \n",
       "WebscopeS5 A1Benchmark-1                                1.000000   \n",
       "           A1Benchmark-10                               1.000000   \n",
       "           A1Benchmark-11                               0.936218   \n",
       "           A1Benchmark-12                               1.000000   \n",
       "           A1Benchmark-13                               0.801014   \n",
       "           A1Benchmark-15                               0.940399   \n",
       "           A1Benchmark-16                               1.000000   \n",
       "           A1Benchmark-18                               0.763889   \n",
       "           A1Benchmark-2                                0.835022   \n",
       "           A1Benchmark-21                               1.000000   \n",
       "           A1Benchmark-22                               0.957058   \n",
       "           A1Benchmark-23                               0.838277   \n",
       "           A1Benchmark-24                               1.000000   \n",
       "           A1Benchmark-25                               1.000000   \n",
       "           A1Benchmark-3                                1.000000   \n",
       "           A1Benchmark-30                               1.000000   \n",
       "           A1Benchmark-33                               1.000000   \n",
       "           A1Benchmark-34                               0.906463   \n",
       "           A1Benchmark-36                               1.000000   \n",
       "           A1Benchmark-39                               0.775553   \n",
       "           A1Benchmark-4                                1.000000   \n",
       "           A1Benchmark-41                               1.000000   \n",
       "           A1Benchmark-42                               1.000000   \n",
       "           A1Benchmark-44                               0.789563   \n",
       "           A1Benchmark-45                               1.000000   \n",
       "           A1Benchmark-49                               0.763889   \n",
       "           A1Benchmark-5                                1.000000   \n",
       "           A1Benchmark-50                               1.000000   \n",
       "           A1Benchmark-53                               1.000000   \n",
       "           A1Benchmark-54                               1.000000   \n",
       "           A1Benchmark-58                               1.000000   \n",
       "           A1Benchmark-6                                0.880659   \n",
       "           A1Benchmark-60                               0.972133   \n",
       "           A1Benchmark-62                               1.000000   \n",
       "           A1Benchmark-63                               0.886222   \n",
       "           A1Benchmark-66                               0.907345   \n",
       "           A1Benchmark-67                               1.000000   \n",
       "           A1Benchmark-8                                1.000000   \n",
       "           A1Benchmark-9                                0.898115   \n",
       "           A2Benchmark-100                              1.000000   \n",
       "           A2Benchmark-11                               1.000000   \n",
       "           A2Benchmark-13                               1.000000   \n",
       "           A2Benchmark-14                               1.000000   \n",
       "           A2Benchmark-16                               0.791667   \n",
       "           A2Benchmark-19                               1.000000   \n",
       "           A2Benchmark-21                               1.000000   \n",
       "           A2Benchmark-23                               1.000000   \n",
       "           A2Benchmark-25                               1.000000   \n",
       "           A2Benchmark-27                               1.000000   \n",
       "           A2Benchmark-28                               1.000000   \n",
       "           A2Benchmark-29                               0.751190   \n",
       "           A2Benchmark-30                               1.000000   \n",
       "           A2Benchmark-31                               1.000000   \n",
       "           A2Benchmark-33                               1.000000   \n",
       "           A2Benchmark-34                               1.000000   \n",
       "           A2Benchmark-35                               1.000000   \n",
       "           A2Benchmark-37                               1.000000   \n",
       "           A2Benchmark-39                               1.000000   \n",
       "           A2Benchmark-41                               1.000000   \n",
       "           A2Benchmark-42                               1.000000   \n",
       "           A2Benchmark-43                               1.000000   \n",
       "           A2Benchmark-44                               1.000000   \n",
       "           A2Benchmark-46                               1.000000   \n",
       "           A2Benchmark-47                               1.000000   \n",
       "           A2Benchmark-48                               1.000000   \n",
       "           A2Benchmark-49                               1.000000   \n",
       "           A2Benchmark-5                                1.000000   \n",
       "           A2Benchmark-51                               1.000000   \n",
       "           A2Benchmark-53                               1.000000   \n",
       "           A2Benchmark-55                               1.000000   \n",
       "           A2Benchmark-56                               1.000000   \n",
       "           A2Benchmark-57                               1.000000   \n",
       "           A2Benchmark-58                               1.000000   \n",
       "           A2Benchmark-59                               1.000000   \n",
       "           A2Benchmark-60                               1.000000   \n",
       "           A2Benchmark-61                               1.000000   \n",
       "           A2Benchmark-62                               1.000000   \n",
       "           A2Benchmark-63                               1.000000   \n",
       "           A2Benchmark-65                               1.000000   \n",
       "           A2Benchmark-67                               1.000000   \n",
       "           A2Benchmark-69                               1.000000   \n",
       "           A2Benchmark-7                                1.000000   \n",
       "           A2Benchmark-70                               1.000000   \n",
       "           A2Benchmark-71                               1.000000   \n",
       "           A2Benchmark-72                               1.000000   \n",
       "           A2Benchmark-73                               1.000000   \n",
       "           A2Benchmark-74                               1.000000   \n",
       "           A2Benchmark-75                               1.000000   \n",
       "           A2Benchmark-76                               1.000000   \n",
       "           A2Benchmark-77                               1.000000   \n",
       "           A2Benchmark-79                               1.000000   \n",
       "           A2Benchmark-81                               1.000000   \n",
       "           A2Benchmark-83                               1.000000   \n",
       "           A2Benchmark-84                               1.000000   \n",
       "           A2Benchmark-85                               1.000000   \n",
       "           A2Benchmark-86                               1.000000   \n",
       "           A2Benchmark-87                               1.000000   \n",
       "           A2Benchmark-88                               1.000000   \n",
       "           A2Benchmark-89                               1.000000   \n",
       "           A2Benchmark-9                                1.000000   \n",
       "           A2Benchmark-90                               1.000000   \n",
       "           A2Benchmark-91                               1.000000   \n",
       "           A2Benchmark-93                               1.000000   \n",
       "           A2Benchmark-94                               0.742857   \n",
       "           A2Benchmark-95                               1.000000   \n",
       "           A2Benchmark-97                               1.000000   \n",
       "           A2Benchmark-98                               1.000000   \n",
       "           A2Benchmark-99                               1.000000   \n",
       "           A3Benchmark-12                               0.715359   \n",
       "           A3Benchmark-23                               0.760246   \n",
       "           A3Benchmark-33                               0.734135   \n",
       "           A3Benchmark-6                                0.846515   \n",
       "           A3Benchmark-64                               1.000000   \n",
       "           A3Benchmark-66                               1.000000   \n",
       "           A3Benchmark-69                               0.780244   \n",
       "           A3Benchmark-7                                0.822195   \n",
       "           A3Benchmark-76                               0.735988   \n",
       "           A3Benchmark-77                               0.828528   \n",
       "           A3Benchmark-86                               0.721699   \n",
       "           A3Benchmark-91                               0.804626   \n",
       "           A3Benchmark-92                               0.754326   \n",
       "\n",
       "                                                                    \\\n",
       "algorithm                                      DeviatingFromMedian   \n",
       "collection dataset                                                   \n",
       "Exathlon   4_1_100000_61-29                               0.995992   \n",
       "           4_1_100000_61-30                               0.995992   \n",
       "           5_1_100000_63-64                               0.996219   \n",
       "           5_1_100000_64-63                               0.987123   \n",
       "GutenTAG   cbf-length-1.semi-supervised                   1.000000   \n",
       "           cbf-length-1.supervised                        1.000000   \n",
       "           cbf-length-1.unsupervised                      1.000000   \n",
       "           cbf-type-extremum.semi-supervised              1.000000   \n",
       "           cbf-type-extremum.supervised                   1.000000   \n",
       "           cbf-type-extremum.unsupervised                 1.000000   \n",
       "           ecg-length-1.semi-supervised                   1.000000   \n",
       "           ecg-length-1.supervised                        1.000000   \n",
       "           ecg-length-1.unsupervised                      1.000000   \n",
       "           ecg-type-extremum.semi-supervised              1.000000   \n",
       "           ecg-type-extremum.supervised                   1.000000   \n",
       "           ecg-type-extremum.unsupervised                 1.000000   \n",
       "           poly-diff-count-1.semi-supervised              1.000000   \n",
       "           poly-diff-count-1.supervised                   1.000000   \n",
       "           poly-diff-count-1.unsupervised                 1.000000   \n",
       "           poly-length-1.semi-supervised                  1.000000   \n",
       "           poly-length-1.supervised                       1.000000   \n",
       "           poly-length-1.unsupervised                     1.000000   \n",
       "           poly-type-platform.semi-supervised             1.000000   \n",
       "           poly-type-platform.supervised                  1.000000   \n",
       "           poly-type-platform.unsupervised                1.000000   \n",
       "           rw-diff-count-1.semi-supervised                1.000000   \n",
       "           rw-diff-count-1.supervised                     1.000000   \n",
       "           rw-diff-count-1.unsupervised                   1.000000   \n",
       "           rw-length-1.semi-supervised                    1.000000   \n",
       "           rw-length-1.supervised                         1.000000   \n",
       "           rw-length-1.unsupervised                       1.000000   \n",
       "           rw-type-extremum.semi-supervised               1.000000   \n",
       "           rw-type-extremum.supervised                    1.000000   \n",
       "           rw-type-extremum.unsupervised                  1.000000   \n",
       "           sinus-length-1.semi-supervised                 1.000000   \n",
       "           sinus-length-1.supervised                      1.000000   \n",
       "           sinus-length-1.unsupervised                    1.000000   \n",
       "           sinus-type-extremum.semi-supervised            1.000000   \n",
       "           sinus-type-extremum.supervised                 1.000000   \n",
       "           sinus-type-extremum.unsupervised               1.000000   \n",
       "NASA-MSL   D-14                                           0.925776   \n",
       "           M-6                                            0.905552   \n",
       "NASA-SMAP  A-6                                            0.787956   \n",
       "WebscopeS5 A1Benchmark-1                                  1.000000   \n",
       "           A1Benchmark-10                                 1.000000   \n",
       "           A1Benchmark-11                                 0.938956   \n",
       "           A1Benchmark-12                                 1.000000   \n",
       "           A1Benchmark-13                                 0.792966   \n",
       "           A1Benchmark-15                                 0.934524   \n",
       "           A1Benchmark-16                                 1.000000   \n",
       "           A1Benchmark-18                                 0.293622   \n",
       "           A1Benchmark-2                                  0.835458   \n",
       "           A1Benchmark-21                                 1.000000   \n",
       "           A1Benchmark-22                                 0.974266   \n",
       "           A1Benchmark-23                                 0.838277   \n",
       "           A1Benchmark-24                                 1.000000   \n",
       "           A1Benchmark-25                                 1.000000   \n",
       "           A1Benchmark-3                                  1.000000   \n",
       "           A1Benchmark-30                                 1.000000   \n",
       "           A1Benchmark-33                                 1.000000   \n",
       "           A1Benchmark-34                                 0.869671   \n",
       "           A1Benchmark-36                                 1.000000   \n",
       "           A1Benchmark-39                                 0.776393   \n",
       "           A1Benchmark-4                                  1.000000   \n",
       "           A1Benchmark-41                                 1.000000   \n",
       "           A1Benchmark-42                                 1.000000   \n",
       "           A1Benchmark-44                                 0.789563   \n",
       "           A1Benchmark-45                                 1.000000   \n",
       "           A1Benchmark-49                                 0.293622   \n",
       "           A1Benchmark-5                                  1.000000   \n",
       "           A1Benchmark-50                                 1.000000   \n",
       "           A1Benchmark-53                                 1.000000   \n",
       "           A1Benchmark-54                                 1.000000   \n",
       "           A1Benchmark-58                                 1.000000   \n",
       "           A1Benchmark-6                                  0.880022   \n",
       "           A1Benchmark-60                                 0.972133   \n",
       "           A1Benchmark-62                                 1.000000   \n",
       "           A1Benchmark-63                                 0.887082   \n",
       "           A1Benchmark-66                                 0.907166   \n",
       "           A1Benchmark-67                                 1.000000   \n",
       "           A1Benchmark-8                                  1.000000   \n",
       "           A1Benchmark-9                                  0.898115   \n",
       "           A2Benchmark-100                                1.000000   \n",
       "           A2Benchmark-11                                 1.000000   \n",
       "           A2Benchmark-13                                 1.000000   \n",
       "           A2Benchmark-14                                 1.000000   \n",
       "           A2Benchmark-16                                 0.791667   \n",
       "           A2Benchmark-19                                 1.000000   \n",
       "           A2Benchmark-21                                 1.000000   \n",
       "           A2Benchmark-23                                 1.000000   \n",
       "           A2Benchmark-25                                 1.000000   \n",
       "           A2Benchmark-27                                 1.000000   \n",
       "           A2Benchmark-28                                 1.000000   \n",
       "           A2Benchmark-29                                 0.771864   \n",
       "           A2Benchmark-30                                 1.000000   \n",
       "           A2Benchmark-31                                 1.000000   \n",
       "           A2Benchmark-33                                 1.000000   \n",
       "           A2Benchmark-34                                 1.000000   \n",
       "           A2Benchmark-35                                 1.000000   \n",
       "           A2Benchmark-37                                 1.000000   \n",
       "           A2Benchmark-39                                 1.000000   \n",
       "           A2Benchmark-41                                 1.000000   \n",
       "           A2Benchmark-42                                 1.000000   \n",
       "           A2Benchmark-43                                 1.000000   \n",
       "           A2Benchmark-44                                 1.000000   \n",
       "           A2Benchmark-46                                 1.000000   \n",
       "           A2Benchmark-47                                 1.000000   \n",
       "           A2Benchmark-48                                 1.000000   \n",
       "           A2Benchmark-49                                 1.000000   \n",
       "           A2Benchmark-5                                  1.000000   \n",
       "           A2Benchmark-51                                 1.000000   \n",
       "           A2Benchmark-53                                 1.000000   \n",
       "           A2Benchmark-55                                 1.000000   \n",
       "           A2Benchmark-56                                 1.000000   \n",
       "           A2Benchmark-57                                 1.000000   \n",
       "           A2Benchmark-58                                 1.000000   \n",
       "           A2Benchmark-59                                 1.000000   \n",
       "           A2Benchmark-60                                 1.000000   \n",
       "           A2Benchmark-61                                 1.000000   \n",
       "           A2Benchmark-62                                 1.000000   \n",
       "           A2Benchmark-63                                 1.000000   \n",
       "           A2Benchmark-65                                 1.000000   \n",
       "           A2Benchmark-67                                 1.000000   \n",
       "           A2Benchmark-69                                 1.000000   \n",
       "           A2Benchmark-7                                  1.000000   \n",
       "           A2Benchmark-70                                 1.000000   \n",
       "           A2Benchmark-71                                 1.000000   \n",
       "           A2Benchmark-72                                 1.000000   \n",
       "           A2Benchmark-73                                 1.000000   \n",
       "           A2Benchmark-74                                 1.000000   \n",
       "           A2Benchmark-75                                 1.000000   \n",
       "           A2Benchmark-76                                 1.000000   \n",
       "           A2Benchmark-77                                 1.000000   \n",
       "           A2Benchmark-79                                 1.000000   \n",
       "           A2Benchmark-81                                 1.000000   \n",
       "           A2Benchmark-83                                 1.000000   \n",
       "           A2Benchmark-84                                 1.000000   \n",
       "           A2Benchmark-85                                 1.000000   \n",
       "           A2Benchmark-86                                 1.000000   \n",
       "           A2Benchmark-87                                 1.000000   \n",
       "           A2Benchmark-88                                 1.000000   \n",
       "           A2Benchmark-89                                 1.000000   \n",
       "           A2Benchmark-9                                  1.000000   \n",
       "           A2Benchmark-90                                 1.000000   \n",
       "           A2Benchmark-91                                 1.000000   \n",
       "           A2Benchmark-93                                 1.000000   \n",
       "           A2Benchmark-94                                 0.742857   \n",
       "           A2Benchmark-95                                 1.000000   \n",
       "           A2Benchmark-97                                 1.000000   \n",
       "           A2Benchmark-98                                 1.000000   \n",
       "           A2Benchmark-99                                 1.000000   \n",
       "           A3Benchmark-12                                 0.715763   \n",
       "           A3Benchmark-23                                 0.764227   \n",
       "           A3Benchmark-33                                 0.718026   \n",
       "           A3Benchmark-6                                  0.846515   \n",
       "           A3Benchmark-64                                 1.000000   \n",
       "           A3Benchmark-66                                 1.000000   \n",
       "           A3Benchmark-69                                 0.780244   \n",
       "           A3Benchmark-7                                  0.824625   \n",
       "           A3Benchmark-76                                 0.764226   \n",
       "           A3Benchmark-77                                 0.828434   \n",
       "           A3Benchmark-86                                 0.724232   \n",
       "           A3Benchmark-91                                 0.804626   \n",
       "           A3Benchmark-92                                 0.751606   \n",
       "\n",
       "                                                         ROC_AUC  \\\n",
       "algorithm                                      DeviatingFromMean   \n",
       "collection dataset                                                 \n",
       "Exathlon   4_1_100000_61-29                             0.998207   \n",
       "           4_1_100000_61-30                             0.998207   \n",
       "           5_1_100000_63-64                             0.999391   \n",
       "           5_1_100000_64-63                             0.998941   \n",
       "GutenTAG   cbf-length-1.semi-supervised                 1.000000   \n",
       "           cbf-length-1.supervised                      1.000000   \n",
       "           cbf-length-1.unsupervised                    1.000000   \n",
       "           cbf-type-extremum.semi-supervised            1.000000   \n",
       "           cbf-type-extremum.supervised                 1.000000   \n",
       "           cbf-type-extremum.unsupervised               1.000000   \n",
       "           ecg-length-1.semi-supervised                 1.000000   \n",
       "           ecg-length-1.supervised                      1.000000   \n",
       "           ecg-length-1.unsupervised                    1.000000   \n",
       "           ecg-type-extremum.semi-supervised            1.000000   \n",
       "           ecg-type-extremum.supervised                 1.000000   \n",
       "           ecg-type-extremum.unsupervised               1.000000   \n",
       "           poly-diff-count-1.semi-supervised            1.000000   \n",
       "           poly-diff-count-1.supervised                 1.000000   \n",
       "           poly-diff-count-1.unsupervised               1.000000   \n",
       "           poly-length-1.semi-supervised                1.000000   \n",
       "           poly-length-1.supervised                     1.000000   \n",
       "           poly-length-1.unsupervised                   1.000000   \n",
       "           poly-type-platform.semi-supervised           1.000000   \n",
       "           poly-type-platform.supervised                1.000000   \n",
       "           poly-type-platform.unsupervised              1.000000   \n",
       "           rw-diff-count-1.semi-supervised              1.000000   \n",
       "           rw-diff-count-1.supervised                   1.000000   \n",
       "           rw-diff-count-1.unsupervised                 1.000000   \n",
       "           rw-length-1.semi-supervised                  1.000000   \n",
       "           rw-length-1.supervised                       1.000000   \n",
       "           rw-length-1.unsupervised                     1.000000   \n",
       "           rw-type-extremum.semi-supervised             1.000000   \n",
       "           rw-type-extremum.supervised                  1.000000   \n",
       "           rw-type-extremum.unsupervised                1.000000   \n",
       "           sinus-length-1.semi-supervised               1.000000   \n",
       "           sinus-length-1.supervised                    1.000000   \n",
       "           sinus-length-1.unsupervised                  1.000000   \n",
       "           sinus-type-extremum.semi-supervised          1.000000   \n",
       "           sinus-type-extremum.supervised               1.000000   \n",
       "           sinus-type-extremum.unsupervised             1.000000   \n",
       "NASA-MSL   D-14                                         0.918919   \n",
       "           M-6                                          0.926337   \n",
       "NASA-SMAP  A-6                                          0.929298   \n",
       "WebscopeS5 A1Benchmark-1                                1.000000   \n",
       "           A1Benchmark-10                               1.000000   \n",
       "           A1Benchmark-11                               0.984729   \n",
       "           A1Benchmark-12                               1.000000   \n",
       "           A1Benchmark-13                               0.982831   \n",
       "           A1Benchmark-15                               0.999020   \n",
       "           A1Benchmark-16                               1.000000   \n",
       "           A1Benchmark-18                               0.998628   \n",
       "           A1Benchmark-2                                0.976458   \n",
       "           A1Benchmark-21                               1.000000   \n",
       "           A1Benchmark-22                               0.981940   \n",
       "           A1Benchmark-23                               0.999286   \n",
       "           A1Benchmark-24                               1.000000   \n",
       "           A1Benchmark-25                               1.000000   \n",
       "           A1Benchmark-3                                1.000000   \n",
       "           A1Benchmark-30                               1.000000   \n",
       "           A1Benchmark-33                               1.000000   \n",
       "           A1Benchmark-34                               0.999497   \n",
       "           A1Benchmark-36                               1.000000   \n",
       "           A1Benchmark-39                               0.990967   \n",
       "           A1Benchmark-4                                1.000000   \n",
       "           A1Benchmark-41                               1.000000   \n",
       "           A1Benchmark-42                               1.000000   \n",
       "           A1Benchmark-44                               0.952097   \n",
       "           A1Benchmark-45                               1.000000   \n",
       "           A1Benchmark-49                               0.998628   \n",
       "           A1Benchmark-5                                1.000000   \n",
       "           A1Benchmark-50                               1.000000   \n",
       "           A1Benchmark-53                               1.000000   \n",
       "           A1Benchmark-54                               1.000000   \n",
       "           A1Benchmark-58                               1.000000   \n",
       "           A1Benchmark-6                                0.986198   \n",
       "           A1Benchmark-60                               0.999654   \n",
       "           A1Benchmark-62                               1.000000   \n",
       "           A1Benchmark-63                               0.997642   \n",
       "           A1Benchmark-66                               0.947510   \n",
       "           A1Benchmark-67                               1.000000   \n",
       "           A1Benchmark-8                                1.000000   \n",
       "           A1Benchmark-9                                0.999570   \n",
       "           A2Benchmark-100                              1.000000   \n",
       "           A2Benchmark-11                               1.000000   \n",
       "           A2Benchmark-13                               1.000000   \n",
       "           A2Benchmark-14                               1.000000   \n",
       "           A2Benchmark-16                               0.999294   \n",
       "           A2Benchmark-19                               1.000000   \n",
       "           A2Benchmark-21                               1.000000   \n",
       "           A2Benchmark-23                               1.000000   \n",
       "           A2Benchmark-25                               1.000000   \n",
       "           A2Benchmark-27                               1.000000   \n",
       "           A2Benchmark-28                               1.000000   \n",
       "           A2Benchmark-29                               0.997639   \n",
       "           A2Benchmark-30                               1.000000   \n",
       "           A2Benchmark-31                               1.000000   \n",
       "           A2Benchmark-33                               1.000000   \n",
       "           A2Benchmark-34                               1.000000   \n",
       "           A2Benchmark-35                               1.000000   \n",
       "           A2Benchmark-37                               1.000000   \n",
       "           A2Benchmark-39                               1.000000   \n",
       "           A2Benchmark-41                               1.000000   \n",
       "           A2Benchmark-42                               1.000000   \n",
       "           A2Benchmark-43                               1.000000   \n",
       "           A2Benchmark-44                               1.000000   \n",
       "           A2Benchmark-46                               1.000000   \n",
       "           A2Benchmark-47                               1.000000   \n",
       "           A2Benchmark-48                               1.000000   \n",
       "           A2Benchmark-49                               1.000000   \n",
       "           A2Benchmark-5                                1.000000   \n",
       "           A2Benchmark-51                               1.000000   \n",
       "           A2Benchmark-53                               1.000000   \n",
       "           A2Benchmark-55                               1.000000   \n",
       "           A2Benchmark-56                               1.000000   \n",
       "           A2Benchmark-57                               1.000000   \n",
       "           A2Benchmark-58                               1.000000   \n",
       "           A2Benchmark-59                               1.000000   \n",
       "           A2Benchmark-60                               1.000000   \n",
       "           A2Benchmark-61                               1.000000   \n",
       "           A2Benchmark-62                               1.000000   \n",
       "           A2Benchmark-63                               1.000000   \n",
       "           A2Benchmark-65                               1.000000   \n",
       "           A2Benchmark-67                               1.000000   \n",
       "           A2Benchmark-69                               1.000000   \n",
       "           A2Benchmark-7                                1.000000   \n",
       "           A2Benchmark-70                               1.000000   \n",
       "           A2Benchmark-71                               1.000000   \n",
       "           A2Benchmark-72                               1.000000   \n",
       "           A2Benchmark-73                               1.000000   \n",
       "           A2Benchmark-74                               1.000000   \n",
       "           A2Benchmark-75                               1.000000   \n",
       "           A2Benchmark-76                               1.000000   \n",
       "           A2Benchmark-77                               1.000000   \n",
       "           A2Benchmark-79                               1.000000   \n",
       "           A2Benchmark-81                               1.000000   \n",
       "           A2Benchmark-83                               1.000000   \n",
       "           A2Benchmark-84                               1.000000   \n",
       "           A2Benchmark-85                               1.000000   \n",
       "           A2Benchmark-86                               1.000000   \n",
       "           A2Benchmark-87                               1.000000   \n",
       "           A2Benchmark-88                               1.000000   \n",
       "           A2Benchmark-89                               1.000000   \n",
       "           A2Benchmark-9                                1.000000   \n",
       "           A2Benchmark-90                               1.000000   \n",
       "           A2Benchmark-91                               1.000000   \n",
       "           A2Benchmark-93                               1.000000   \n",
       "           A2Benchmark-94                               0.998941   \n",
       "           A2Benchmark-95                               1.000000   \n",
       "           A2Benchmark-97                               1.000000   \n",
       "           A2Benchmark-98                               1.000000   \n",
       "           A2Benchmark-99                               1.000000   \n",
       "           A3Benchmark-12                               0.967883   \n",
       "           A3Benchmark-23                               0.970330   \n",
       "           A3Benchmark-33                               0.989533   \n",
       "           A3Benchmark-6                                0.993628   \n",
       "           A3Benchmark-64                               1.000000   \n",
       "           A3Benchmark-66                               1.000000   \n",
       "           A3Benchmark-69                               0.996414   \n",
       "           A3Benchmark-7                                0.995701   \n",
       "           A3Benchmark-76                               0.988776   \n",
       "           A3Benchmark-77                               0.974647   \n",
       "           A3Benchmark-86                               0.966545   \n",
       "           A3Benchmark-91                               0.977313   \n",
       "           A3Benchmark-92                               0.993496   \n",
       "\n",
       "                                                                    \n",
       "algorithm                                      DeviatingFromMedian  \n",
       "collection dataset                                                  \n",
       "Exathlon   4_1_100000_61-29                               0.999029  \n",
       "           4_1_100000_61-30                               0.999029  \n",
       "           5_1_100000_63-64                               0.999731  \n",
       "           5_1_100000_64-63                               0.999165  \n",
       "GutenTAG   cbf-length-1.semi-supervised                   1.000000  \n",
       "           cbf-length-1.supervised                        1.000000  \n",
       "           cbf-length-1.unsupervised                      1.000000  \n",
       "           cbf-type-extremum.semi-supervised              1.000000  \n",
       "           cbf-type-extremum.supervised                   1.000000  \n",
       "           cbf-type-extremum.unsupervised                 1.000000  \n",
       "           ecg-length-1.semi-supervised                   1.000000  \n",
       "           ecg-length-1.supervised                        1.000000  \n",
       "           ecg-length-1.unsupervised                      1.000000  \n",
       "           ecg-type-extremum.semi-supervised              1.000000  \n",
       "           ecg-type-extremum.supervised                   1.000000  \n",
       "           ecg-type-extremum.unsupervised                 1.000000  \n",
       "           poly-diff-count-1.semi-supervised              1.000000  \n",
       "           poly-diff-count-1.supervised                   1.000000  \n",
       "           poly-diff-count-1.unsupervised                 1.000000  \n",
       "           poly-length-1.semi-supervised                  1.000000  \n",
       "           poly-length-1.supervised                       1.000000  \n",
       "           poly-length-1.unsupervised                     1.000000  \n",
       "           poly-type-platform.semi-supervised             1.000000  \n",
       "           poly-type-platform.supervised                  1.000000  \n",
       "           poly-type-platform.unsupervised                1.000000  \n",
       "           rw-diff-count-1.semi-supervised                1.000000  \n",
       "           rw-diff-count-1.supervised                     1.000000  \n",
       "           rw-diff-count-1.unsupervised                   1.000000  \n",
       "           rw-length-1.semi-supervised                    1.000000  \n",
       "           rw-length-1.supervised                         1.000000  \n",
       "           rw-length-1.unsupervised                       1.000000  \n",
       "           rw-type-extremum.semi-supervised               1.000000  \n",
       "           rw-type-extremum.supervised                    1.000000  \n",
       "           rw-type-extremum.unsupervised                  1.000000  \n",
       "           sinus-length-1.semi-supervised                 1.000000  \n",
       "           sinus-length-1.supervised                      1.000000  \n",
       "           sinus-length-1.unsupervised                    1.000000  \n",
       "           sinus-type-extremum.semi-supervised            1.000000  \n",
       "           sinus-type-extremum.supervised                 1.000000  \n",
       "           sinus-type-extremum.unsupervised               1.000000  \n",
       "NASA-MSL   D-14                                           0.918919  \n",
       "           M-6                                            0.962332  \n",
       "NASA-SMAP  A-6                                            0.929298  \n",
       "WebscopeS5 A1Benchmark-1                                  1.000000  \n",
       "           A1Benchmark-10                                 1.000000  \n",
       "           A1Benchmark-11                                 0.993291  \n",
       "           A1Benchmark-12                                 1.000000  \n",
       "           A1Benchmark-13                                 0.979999  \n",
       "           A1Benchmark-15                                 0.998740  \n",
       "           A1Benchmark-16                                 1.000000  \n",
       "           A1Benchmark-18                                 0.988112  \n",
       "           A1Benchmark-2                                  0.977117  \n",
       "           A1Benchmark-21                                 1.000000  \n",
       "           A1Benchmark-22                                 0.998468  \n",
       "           A1Benchmark-23                                 0.999286  \n",
       "           A1Benchmark-24                                 1.000000  \n",
       "           A1Benchmark-25                                 1.000000  \n",
       "           A1Benchmark-3                                  1.000000  \n",
       "           A1Benchmark-30                                 1.000000  \n",
       "           A1Benchmark-33                                 1.000000  \n",
       "           A1Benchmark-34                                 0.999396  \n",
       "           A1Benchmark-36                                 1.000000  \n",
       "           A1Benchmark-39                                 0.991602  \n",
       "           A1Benchmark-4                                  1.000000  \n",
       "           A1Benchmark-41                                 1.000000  \n",
       "           A1Benchmark-42                                 1.000000  \n",
       "           A1Benchmark-44                                 0.952097  \n",
       "           A1Benchmark-45                                 1.000000  \n",
       "           A1Benchmark-49                                 0.988112  \n",
       "           A1Benchmark-5                                  1.000000  \n",
       "           A1Benchmark-50                                 1.000000  \n",
       "           A1Benchmark-53                                 1.000000  \n",
       "           A1Benchmark-54                                 1.000000  \n",
       "           A1Benchmark-58                                 1.000000  \n",
       "           A1Benchmark-6                                  0.984364  \n",
       "           A1Benchmark-60                                 0.999654  \n",
       "           A1Benchmark-62                                 1.000000  \n",
       "           A1Benchmark-63                                 0.997729  \n",
       "           A1Benchmark-66                                 0.941265  \n",
       "           A1Benchmark-67                                 1.000000  \n",
       "           A1Benchmark-8                                  1.000000  \n",
       "           A1Benchmark-9                                  0.999570  \n",
       "           A2Benchmark-100                                1.000000  \n",
       "           A2Benchmark-11                                 1.000000  \n",
       "           A2Benchmark-13                                 1.000000  \n",
       "           A2Benchmark-14                                 1.000000  \n",
       "           A2Benchmark-16                                 0.999294  \n",
       "           A2Benchmark-19                                 1.000000  \n",
       "           A2Benchmark-21                                 1.000000  \n",
       "           A2Benchmark-23                                 1.000000  \n",
       "           A2Benchmark-25                                 1.000000  \n",
       "           A2Benchmark-27                                 1.000000  \n",
       "           A2Benchmark-28                                 1.000000  \n",
       "           A2Benchmark-29                                 0.998111  \n",
       "           A2Benchmark-30                                 1.000000  \n",
       "           A2Benchmark-31                                 1.000000  \n",
       "           A2Benchmark-33                                 1.000000  \n",
       "           A2Benchmark-34                                 1.000000  \n",
       "           A2Benchmark-35                                 1.000000  \n",
       "           A2Benchmark-37                                 1.000000  \n",
       "           A2Benchmark-39                                 1.000000  \n",
       "           A2Benchmark-41                                 1.000000  \n",
       "           A2Benchmark-42                                 1.000000  \n",
       "           A2Benchmark-43                                 1.000000  \n",
       "           A2Benchmark-44                                 1.000000  \n",
       "           A2Benchmark-46                                 1.000000  \n",
       "           A2Benchmark-47                                 1.000000  \n",
       "           A2Benchmark-48                                 1.000000  \n",
       "           A2Benchmark-49                                 1.000000  \n",
       "           A2Benchmark-5                                  1.000000  \n",
       "           A2Benchmark-51                                 1.000000  \n",
       "           A2Benchmark-53                                 1.000000  \n",
       "           A2Benchmark-55                                 1.000000  \n",
       "           A2Benchmark-56                                 1.000000  \n",
       "           A2Benchmark-57                                 1.000000  \n",
       "           A2Benchmark-58                                 1.000000  \n",
       "           A2Benchmark-59                                 1.000000  \n",
       "           A2Benchmark-60                                 1.000000  \n",
       "           A2Benchmark-61                                 1.000000  \n",
       "           A2Benchmark-62                                 1.000000  \n",
       "           A2Benchmark-63                                 1.000000  \n",
       "           A2Benchmark-65                                 1.000000  \n",
       "           A2Benchmark-67                                 1.000000  \n",
       "           A2Benchmark-69                                 1.000000  \n",
       "           A2Benchmark-7                                  1.000000  \n",
       "           A2Benchmark-70                                 1.000000  \n",
       "           A2Benchmark-71                                 1.000000  \n",
       "           A2Benchmark-72                                 1.000000  \n",
       "           A2Benchmark-73                                 1.000000  \n",
       "           A2Benchmark-74                                 1.000000  \n",
       "           A2Benchmark-75                                 1.000000  \n",
       "           A2Benchmark-76                                 1.000000  \n",
       "           A2Benchmark-77                                 1.000000  \n",
       "           A2Benchmark-79                                 1.000000  \n",
       "           A2Benchmark-81                                 1.000000  \n",
       "           A2Benchmark-83                                 1.000000  \n",
       "           A2Benchmark-84                                 1.000000  \n",
       "           A2Benchmark-85                                 1.000000  \n",
       "           A2Benchmark-86                                 1.000000  \n",
       "           A2Benchmark-87                                 1.000000  \n",
       "           A2Benchmark-88                                 1.000000  \n",
       "           A2Benchmark-89                                 1.000000  \n",
       "           A2Benchmark-9                                  1.000000  \n",
       "           A2Benchmark-90                                 1.000000  \n",
       "           A2Benchmark-91                                 1.000000  \n",
       "           A2Benchmark-93                                 1.000000  \n",
       "           A2Benchmark-94                                 0.998941  \n",
       "           A2Benchmark-95                                 1.000000  \n",
       "           A2Benchmark-97                                 1.000000  \n",
       "           A2Benchmark-98                                 1.000000  \n",
       "           A2Benchmark-99                                 1.000000  \n",
       "           A3Benchmark-12                                 0.968529  \n",
       "           A3Benchmark-23                                 0.969489  \n",
       "           A3Benchmark-33                                 0.989010  \n",
       "           A3Benchmark-6                                  0.993628  \n",
       "           A3Benchmark-64                                 1.000000  \n",
       "           A3Benchmark-66                                 1.000000  \n",
       "           A3Benchmark-69                                 0.996414  \n",
       "           A3Benchmark-7                                  0.996179  \n",
       "           A3Benchmark-76                                 0.988776  \n",
       "           A3Benchmark-77                                 0.974647  \n",
       "           A3Benchmark-86                                 0.966314  \n",
       "           A3Benchmark-91                                 0.977313  \n",
       "           A3Benchmark-92                                 0.993645  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df.pivot_table(index=[\"collection\", \"dataset\"], columns=\"algorithm\", values=[\"ROC_AUC\", \"PR_AUC\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp[(df_tmp[(\"ROC_AUC\", \"DeviatingFromMean\")] > 0.9) & (df_tmp[(\"PR_AUC\", \"DeviatingFromMean\")] > 0.7)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80e163ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_scores([\"DeviatingFromMean\", \"DeviatingFromMedian\"], (\"NASA-SMAP\", \"A-6\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72e14427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_scores([\"DeviatingFromMean\", \"DeviatingFromMedian\"], (\"Exathlon\", \"4_1_100000_61-29\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d51090dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_scores([\"DeviatingFromMean\", \"DeviatingFromMedian\"], (\"WADI\", \"WADI.A2-2\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686e6a6f",
   "metadata": {},
   "source": [
    "## Compare to algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8fa27601",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(root_path / \"results\" / \"paper-plots-results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "43e61236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Algorithms that are worse than the baselines based on ROC_AUC"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>PR_AUC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "      <td>0.605568</td>\n",
       "      <td>0.191578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMean</th>\n",
       "      <td>0.601494</td>\n",
       "      <td>0.192754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSA</th>\n",
       "      <td>0.598004</td>\n",
       "      <td>0.357341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LaserDBN</th>\n",
       "      <td>0.595319</td>\n",
       "      <td>0.112042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOT SAX</th>\n",
       "      <td>0.594816</td>\n",
       "      <td>0.363216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSBitmap</th>\n",
       "      <td>0.588910</td>\n",
       "      <td>0.044869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCC</th>\n",
       "      <td>0.588195</td>\n",
       "      <td>0.150876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSPOT</th>\n",
       "      <td>0.551196</td>\n",
       "      <td>0.261378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFT</th>\n",
       "      <td>0.544929</td>\n",
       "      <td>0.416914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RobustPCA</th>\n",
       "      <td>0.542928</td>\n",
       "      <td>0.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagel</th>\n",
       "      <td>0.540086</td>\n",
       "      <td>0.046918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-H-ESD (Twitter)</th>\n",
       "      <td>0.528855</td>\n",
       "      <td>0.278541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAnoGan</th>\n",
       "      <td>0.514011</td>\n",
       "      <td>0.134715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-CNN</th>\n",
       "      <td>0.509170</td>\n",
       "      <td>0.404871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TARZAN</th>\n",
       "      <td>0.502058</td>\n",
       "      <td>0.132964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hybrid KNN</th>\n",
       "      <td>0.496522</td>\n",
       "      <td>0.137429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultiHMM</th>\n",
       "      <td>0.476958</td>\n",
       "      <td>0.072319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ROC_AUC    PR_AUC\n",
       "algorithm                              \n",
       "DeviatingFromMedian  0.605568  0.191578\n",
       "DeviatingFromMean    0.601494  0.192754\n",
       "SSA                  0.598004  0.357341\n",
       "LaserDBN             0.595319  0.112042\n",
       "HOT SAX              0.594816  0.363216\n",
       "TSBitmap             0.588910  0.044869\n",
       "PCC                  0.588195  0.150876\n",
       "DSPOT                0.551196  0.261378\n",
       "FFT                  0.544929  0.416914\n",
       "RobustPCA            0.542928  0.190476\n",
       "Bagel                0.540086  0.046918\n",
       "S-H-ESD (Twitter)    0.528855  0.278541\n",
       "TAnoGan              0.514011  0.134715\n",
       "SR-CNN               0.509170  0.404871\n",
       "TARZAN               0.502058  0.132964\n",
       "Hybrid KNN           0.496522  0.137429\n",
       "MultiHMM             0.476958  0.072319"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Algorithms that are worse than the baselines based on PR_AUC"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>PR_AUC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMean</th>\n",
       "      <td>0.601494</td>\n",
       "      <td>0.192754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviatingFromMedian</th>\n",
       "      <td>0.605568</td>\n",
       "      <td>0.191578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RobustPCA</th>\n",
       "      <td>0.542928</td>\n",
       "      <td>0.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torsk</th>\n",
       "      <td>0.682339</td>\n",
       "      <td>0.188936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series2Graph</th>\n",
       "      <td>0.749552</td>\n",
       "      <td>0.181638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OceanWNN</th>\n",
       "      <td>0.677339</td>\n",
       "      <td>0.171451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCC</th>\n",
       "      <td>0.588195</td>\n",
       "      <td>0.150876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hybrid KNN</th>\n",
       "      <td>0.496522</td>\n",
       "      <td>0.137429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAnoGan</th>\n",
       "      <td>0.514011</td>\n",
       "      <td>0.134715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TARZAN</th>\n",
       "      <td>0.502058</td>\n",
       "      <td>0.132964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PST</th>\n",
       "      <td>0.637753</td>\n",
       "      <td>0.124748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OmniAnomaly</th>\n",
       "      <td>0.629753</td>\n",
       "      <td>0.119589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LaserDBN</th>\n",
       "      <td>0.595319</td>\n",
       "      <td>0.112042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hybrid Isolation Forest (HIF)</th>\n",
       "      <td>0.605925</td>\n",
       "      <td>0.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumentaHTM</th>\n",
       "      <td>0.715285</td>\n",
       "      <td>0.107537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultiHMM</th>\n",
       "      <td>0.476958</td>\n",
       "      <td>0.072319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagel</th>\n",
       "      <td>0.540086</td>\n",
       "      <td>0.046918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSBitmap</th>\n",
       "      <td>0.588910</td>\n",
       "      <td>0.044869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                ROC_AUC    PR_AUC\n",
       "algorithm                                        \n",
       "DeviatingFromMean              0.601494  0.192754\n",
       "DeviatingFromMedian            0.605568  0.191578\n",
       "RobustPCA                      0.542928  0.190476\n",
       "Torsk                          0.682339  0.188936\n",
       "Series2Graph                   0.749552  0.181638\n",
       "OceanWNN                       0.677339  0.171451\n",
       "PCC                            0.588195  0.150876\n",
       "Hybrid KNN                     0.496522  0.137429\n",
       "TAnoGan                        0.514011  0.134715\n",
       "TARZAN                         0.502058  0.132964\n",
       "PST                            0.637753  0.124748\n",
       "OmniAnomaly                    0.629753  0.119589\n",
       "LaserDBN                       0.595319  0.112042\n",
       "Hybrid Isolation Forest (HIF)  0.605925  0.108124\n",
       "NumentaHTM                     0.715285  0.107537\n",
       "MultiHMM                       0.476958  0.072319\n",
       "Bagel                          0.540086  0.046918\n",
       "TSBitmap                       0.588910  0.044869"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df2.copy()#[(df2[\"collection\"] == \"WebscopeS5\") & (df2[\"dataset\"] == \"A1Benchmark-30\")]\n",
    "df_tmp = df_tmp.groupby(by=\"algorithm\")[[\"ROC_AUC\", \"PR_AUC\"]].mean()\n",
    "df_tmp = pd.concat([df_tmp, df.groupby(by=\"algorithm\")[[\"ROC_AUC\", \"PR_AUC\"]].mean()])\n",
    "df_tmp = df_tmp.sort_values(\"ROC_AUC\", ascending=False)\n",
    "\n",
    "display(Markdown(\"### Algorithms that are worse than the baselines based on ROC_AUC\"))\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp[df_tmp[\"ROC_AUC\"] <= df_tmp.loc[\"DeviatingFromMedian\", \"ROC_AUC\"]])\n",
    "\n",
    "display(Markdown(\"### Algorithms that are worse than the baselines based on PR_AUC\"))\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp[df_tmp[\"PR_AUC\"] <= df_tmp.loc[\"DeviatingFromMean\", \"PR_AUC\"]].sort_values(\"PR_AUC\", ascending=False))"
   ]
  },
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   "cell_type": "markdown",
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   "source": []
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